Transition to Practice Award Lecture

Sirish L. Shah

University of Alberta

Sirish L. Shah is on faculty with the Department of Chemical and Materials Engineering at the University of Alberta, where he held the NSERC-Matrikon-Suncor-iCORE Senior Industrial Research Chair in Computer Process Control from 2000 to 2012. He has held visiting appointments at Oxford University and Balliol College as a SERC fellow, Kumamoto University (Japan) as a Senior Research Fellow of the Japan Society for the Promotion of Science, the University of Newcastle, Australia, IIT-Madras India, and the National University of Singapore. He was the recipient of the Albright & Wilson Americas Award of the Canadian Society for Chemical Engineering (CSChE) in recognition of distinguished contributions to chemical engineering in 1989, the Killam Professor in 2003, the D.G. Fisher Award of the CSChE for significant contributions in the field of systems and control and the ASTECH "Innovations Prize in Oil Sands Research" in 2011. He is a fellow of the Canadian Academy of Engineering (FCAE) and holds a honorary doctorate from Kumamoto University, Japan. He has supervised more than 80 graduate students and published 185 referred journal papers.

The main areas of his current research are process and performance monitoring, system identification and design, analysis and rationalization of alarm systems. He has co-authored three books: "Performance Assessment of Control Loops: Theory and Applications", "Diagnosis of Process Nonlinearities and Valve Stiction: Data Driven Approaches", and a more recent brief monograph on "Capturing Connectivity and Causality in Complex Industrial Processes". Results from Shah's research group have been translated into commercial software for process and performance monitoring. He has consulted widely for the process industry and control software vendors.

Surfing the digital tsunami with data analytics tools

Process data analytic methods rely on the notion of sensor fusion whereby data from many sensors or units are combined with process information, such as physical connectivity of process units, to give a holistic picture of health of an integrated plant. Typical analytic methods require the execution of following steps: i) understanding the process and the purpose of the analytics exercise; ii) data collection and quality assessment; iii) outlier detection, noise filtering and data reconciliation; iv) data segmentation followed by process and performance monitoring including root cause detection of faults or development of an inferential soft-sensor. The entire process is iterative and may require re-visiting earlier steps again and again.

For efficient and informative analytics data analysis is ideally carried out, in the temporal as well as spectral domains, on a multitude and NOT singular sensor signals to detect process abnormality. Such multivariate process data analytics involves information extraction from routine process data, that is typically non-categorical, plus alarm data which is mainly in binary form and process connectivity information that can be inferred from the data through causality analysis or as obtained from piping and instrument diagrams of a process.

This talk will present an overview of the analytics platform and the visualization and analysis tools that have been developed at the University of Alberta and successfully applied to industrial processes. Several industrial case studies related to data-based controller performance monitoring; root cause analysis of plant-wide oscillations and abnormal situations monitoring will serve as illustrative examples of the promise of data analytics as applied to a variety of different processes.